Tidy Tuesday 2
Load Libraries
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Load Data
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Introduction
The goal of this second Tidy Tuesday is to create a pie chart distribution of the average number of persons per race from the years 1790 - 1830 and from 1831 - 1870.
I will start by learning how to create a pi chart and include my examples to keep record of my progress. Skip to “Create pi chart using the Census data” for my figures.
If I am unable to do this, I will create a single pie chart of all years.
Learing how to create a pi chart
Functions:
“x” is a vector containing the numeric values used in the pie chart
“labels” is used to give description to the slices.
“main” indicates the title of the chart
“col” indicates the color palette
“clockwise” is a logical value indicating if the slices are drawn clockwise or counterclockwise
“int.angle” determines the starting angle of the slices, from 0 to 360.
“$” dollar sign is a way to reference a column.
Practicing using pie chart function
## Warning in brewer.pal(length(count), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels
Example
count<- c(7, 25, 16, 12, 10,30)
pie(count, #creates pie chart as 1 - 6 counterclockwise
clockwise = TRUE, #makes clockwise
labels = count, #shows actual values of counts
col = color)Creating a 3D pi Chart
library(plotrix)
slices <- c(10, 12, 4, 16, 8) #assigning random numbers
lbls <- c("US", "UK", "Australia", "Germany", "France") #Naming slices
pie3D(slices,labels=lbls,explode=0.1, # makes pi charts 3D
main="Pie Chart of Countries ") #giving title Creating pie chart with example data
# Pie Chart from data frame with Appended Sample Sizes
mytable <- table(iris$Species)
lbls <- paste(names(mytable), "\n", mytable, sep="")
pie(mytable, labels = lbls,
main="Pie Chart of Species\n (with sample sizes)")Create pi chart using the Census data
Pivot data
view(Census)
R_pivot <- Census %>%
pivot_longer(cols = total:black_slaves, # the cols you want to pivot. This says select the all the types of persons positions/ status's.
names_to = "Person_Status", # put the names of all person's status under one column
values_to = "Values") %>% # put the number of persons in each catergory under values column
filter(region == "USA Total") #look at national level data only
view(R_pivot)Manipulate data for two different time periods
Create Pi Charts
# Pie chart with plotly for all person's statuses in 1790
a <- plot_ly(data = data, labels = ~Person_Status, values = ~Values, # load in data, labels to status, values to number of persons under each category
type = 'pie', sort= FALSE, #create pie chart
marker= list(colors=colors, line = list(color="black", width=1))) %>% #fills in pie slices
layout(title="Person's Status in the United States, 1790") #add title
a # Pie chart with plotly for all person's statuses in 1870
b <- plot_ly(data = data2, labels = ~Person_Status, values = ~Values, # load in data, labels to status, values to number of persons under each category
type = 'pie', sort= FALSE, #create pie chart
marker= list(colors=colors, line = list(color="black", width=1))) %>% #fills in pie slices
layout(title="Person's Status in the United States, 1870") #add title
bCreate column figure with Census data
R_pivot %>%
ggplot(aes(y = Values, #Create chart of persons over time in the united states
x = year,
color = Person_Status))+
geom_col()+ # create column plot
labs(title = "Slavery in the United States", #add titles and labels
subtitle = "Fiscal Years 1790 - 1870",
caption = " Anthony Starks, Allen Hillery Sekou Tyler \n https://github.com/rfordatascience/tidytuesday/tree/master/data/2021/2021-02-16.", #site data source
x = "Year", y = "Persons")+ #rename axis's
theme_minimal() + #add theme
ggsave(here("TidyT_2","outputs", "Slavery in the United States.png"), #save to outputs
width = 7, height = 5)